1 Tidyverse & tidy data

1.1 Tidyverse

install.packages("tidyverse")

1.2 Tidyverse

require(tidyverse)
## Loading required package: tidyverse
## ── Attaching packages ─────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.1.0     ✔ purrr   0.2.5
## ✔ tibble  2.0.1     ✔ dplyr   0.7.8
## ✔ tidyr   0.8.2     ✔ stringr 1.4.0
## ✔ readr   1.3.1     ✔ forcats 0.3.0
## ── Conflicts ────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

1.3 Tidy data

1.4 Why tidying a dataset ?

boxplot(treatments)

1.5 Why tidying a dataset ?

treatments.2 <- t(treatments)
boxplot(treatments.2)

1.6 Why tidying a dataset ?

boxplot(result~treatment,data=treatments.3)

1.7 Why tidying a dataset ?

boxplot(result~person,data=treatments.3)

boxplot(result~person+treatment,data=treatments.3)

2 Pipe and tibble

2.1 Pipes

The pipe %>%:

  • Come from the package by Stefan Milton Bache.
  • Automatically loaded in tidyverse.
  • Equivalent to | in bash
cat iris.tsv | cut -f5 | sed 's/^./\U&/' | head
## Species
## Setosa
## Setosa
## Setosa
## Setosa
## Setosa
## Setosa
## Setosa
## Setosa
## Setosa
read_tsv("iris.tsv",col_names = F) %>% pull(5) %>% str_to_title() %>% head
## [1] "Species" "Setosa"  "Setosa"  "Setosa"  "Setosa"  "Setosa"

2.2 Pipes vs no pipes : without pipe

ex.dat <- rnorm(n = 1000,mean = 5,sd=1)
sub.dat <- sample(ex.dat,size = 100,replace=F)
hist(sub.dat)

2.3 Pipes vs no pipes : without pipe

hist(sample(rnorm(n = 1000,mean = 5,sd=1),size = 100,replace=F))

2.4 Pipes vs no pipes : with pipe

rnorm(n = 1000,mean = 5,sd=1) %>%
    sample(size = 100,replace=F) %>%
    hist()

2.5 Tibbles: tibble::tibble

my.tibble <- tibble(
    person = c("John Smith","Jane Doe","Mary Johnson"),
    treatment_a = sample(10:25,size = 3,replace = T),
    treatment_b = sample(10:25,size = 3,replace = T),
    treatment_c = sample(10:25,size = 3,replace = T)
)
## # A tibble: 3 x 4
##   person       treatment_a treatment_b treatment_c
##   <chr>              <int>       <int>       <int>
## 1 John Smith            18          12          15
## 2 Jane Doe              17          15          17
## 3 Mary Johnson          14          14          15

2.6 Tibbles: tibble::tibble()

Pros:

  • Cells can contain list and data.frame.
  • It never changes an input’s type (i.e., no more stringsAsFactors = FALSE!).
  • Can use not valid R variable names (:)) as column names.
  • Refined print method that shows only the first 10 rows.

Cons:

  • It never uses row.names().
    • use tibble::rownames_to_column().
treatments %>% tibble::rownames_to_column("person") %>%
    as_tibble() %>%
    rename(`:)` = person)
## # A tibble: 3 x 4
##   `:)`         treatment_a treatment_b treatment_c
##   <chr>              <int>       <int>       <int>
## 1 John Smith            16          22          12
## 2 Jane Doe              22          19          22
## 3 Mary Johnson          17          14          14

2.7 Split a dataset into multiples dataset using tidyr::nest()

my.tibble.split <- my.tibble %>% group_by(person) %>% nest(.key = Data)
my.tibble.split
## # A tibble: 3 x 2
##   person       Data            
##   <chr>        <list>          
## 1 John Smith   <tibble [1 × 3]>
## 2 Jane Doe     <tibble [1 × 3]>
## 3 Mary Johnson <tibble [1 × 3]>
my.tibble.split[["Data"]][[1]]
## # A tibble: 1 x 3
##   treatment_a treatment_b treatment_c
##         <int>       <int>       <int>
## 1          18          12          15

2.8 Read/write tibbles: readr::read_* / readr::write_*

Read/write tibbles: :

  • readr::read_*:

    • read_csv: comma delimited files.
    • read_csv2: semi-colon delimited files.
    • read_tsv: tab delimited files.
    • read_delim: any delimiter.

  • readr::write_*:

    • write_csv: comma delimited files.
    • write_csv2: semi-colon delimited files.
    • write_tsv: tab delimited files.
    • write_delim: any delimiter.

2.9 Read/write tibbles: readr::read_* / readr::write_*

read_tsv("iris.tsv",
         col_types = cols(
            Sepal.Length = col_double(),Sepal.Width = col_double(),
            Petal.Length = col_double(),Petal.Width = col_double(),
            Species = col_character()
         )
    )
## # A tibble: 150 x 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##           <dbl>       <dbl>        <dbl>       <dbl> <chr>  
##  1          5.1         3.5          1.4         0.2 setosa 
##  2          4.9         3            1.4         0.2 setosa 
##  3          4.7         3.2          1.3         0.2 setosa 
##  4          4.6         3.1          1.5         0.2 setosa 
##  5          5           3.6          1.4         0.2 setosa 
##  6          5.4         3.9          1.7         0.4 setosa 
##  7          4.6         3.4          1.4         0.3 setosa 
##  8          5           3.4          1.5         0.2 setosa 
##  9          4.4         2.9          1.4         0.2 setosa 
## 10          4.9         3.1          1.5         0.1 setosa 
## # … with 140 more rows

3 Tidying dataset

3.1 Using tidyr to reshape data

Use tidyr::gather() and tidyr::spread() to reorganize the value.

treatments %>% tibble::rownames_to_column("person") %>%
    tidyr::gather(key="treatment",value = "result",-person)
##         person   treatment result
## 1   John Smith treatment_a     16
## 2     Jane Doe treatment_a     22
## 3 Mary Johnson treatment_a     17
## 4   John Smith treatment_b     22
## 5     Jane Doe treatment_b     19
## 6 Mary Johnson treatment_b     14
## 7   John Smith treatment_c     12
## 8     Jane Doe treatment_c     22
## 9 Mary Johnson treatment_c     14
treatment.3 %>% spread(key = "treatment",value = "result")
##         person treatment_a treatment_b treatment_c
## 1     Jane Doe          22          19          22
## 2   John Smith          16          22          12
## 3 Mary Johnson          17          14          14

3.2 Using tidyr to reshape data

treatment.3 %>% separate(person,into = c("First Name","Last Name"),sep=" ")
##   First Name Last Name   treatment result
## 1       John     Smith treatment_a     16
## 2       Jane       Doe treatment_a     22
## 3       Mary   Johnson treatment_a     17
## 4       John     Smith treatment_b     22
## 5       Jane       Doe treatment_b     19
## 6       Mary   Johnson treatment_b     14
## 7       John     Smith treatment_c     12
## 8       Jane       Doe treatment_c     22
## 9       Mary   Johnson treatment_c     14

4 Manipulate a dataset using dplyr

4.1 dplyr

dplyr:

  • Manipulate variables.
  • Manipulate observations.
  • Group / summarise observations.

4.1.1 iris dataset

The famous (Fisher’s or Anderson’s) iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica. (from ?iris)

4.2 Manipulate variables using dplyr::select()

iris.tbl %>% select(1:3)
iris.tbl %>% select(Sepal.Length,Sepal.Width,Petal.Length)
iris.tbl %>% select(Sepal.Length:Petal.Length)
iris.tbl %>% select(-Petal.Width,-Species)
## # A tibble: 150 x 3
##    Sepal.Length Sepal.Width Petal.Length
##           <dbl>       <dbl>        <dbl>
##  1          5.1         3.5          1.4
##  2          4.9         3            1.4
##  3          4.7         3.2          1.3
##  4          4.6         3.1          1.5
##  5          5           3.6          1.4
##  6          5.4         3.9          1.7
##  7          4.6         3.4          1.4
##  8          5           3.4          1.5
##  9          4.4         2.9          1.4
## 10          4.9         3.1          1.5
## # … with 140 more rows

4.3 Manipulate variables using dplyr::select()

iris.tbl %>% select(starts_with("Petal"))
## # A tibble: 150 x 2
##    Petal.Length Petal.Width
##           <dbl>       <dbl>
##  1          1.4         0.2
##  2          1.4         0.2
##  3          1.3         0.2
##  4          1.5         0.2
##  5          1.4         0.2
##  6          1.7         0.4
##  7          1.4         0.3
##  8          1.5         0.2
##  9          1.4         0.2
## 10          1.5         0.1
## # … with 140 more rows

4.4 Manipulate variables using dplyr::pull()

iris.tbl %>% select(Sepal.Length)
## # A tibble: 150 x 1
##   Sepal.Length
##          <dbl>
## 1          5.1
## 2          4.9
## 3          4.7
## 4          4.6
## 5          5  
## # … with 145 more rows
iris.tbl %>% pull(Sepal.Length)
## [1] 5.1 4.9 4.7 4.6 5.0

4.5 Modify variables using dplyr::mutate()

iris.tbl %>%
    mutate(Petal.Length = 
               scales::percent(Petal.Length/max(Petal.Length))
           )
## # A tibble: 150 x 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##           <dbl>       <dbl> <chr>              <dbl> <fct>  
##  1          5.1         3.5 20.3%                0.2 setosa 
##  2          4.9         3   20.3%                0.2 setosa 
##  3          4.7         3.2 18.8%                0.2 setosa 
##  4          4.6         3.1 21.7%                0.2 setosa 
##  5          5           3.6 20.3%                0.2 setosa 
##  6          5.4         3.9 24.6%                0.4 setosa 
##  7          4.6         3.4 20.3%                0.3 setosa 
##  8          5           3.4 21.7%                0.2 setosa 
##  9          4.4         2.9 20.3%                0.2 setosa 
## 10          4.9         3.1 21.7%                0.1 setosa 
## # … with 140 more rows

4.6 Create variables using dplyr::mutate()

iris.tbl %>%
    #Case 1
    mutate(Size = dplyr::case_when(
            Petal.Length >= 4 & Petal.Width >= 1.3 ~ "Big",
            Petal.Length < 4 & Petal.Width < 1.3 ~ "Small"
        )
    ) %>%
    #Case 2
    mutate(Ratio_Petal_Sepal_width = Petal.Width/Sepal.Width)
## # A tibble: 3 x 7
##   Ratio_Petal_Sep… Size  Sepal.Length Sepal.Width Petal.Length Petal.Width
##              <dbl> <chr>        <dbl>       <dbl>        <dbl>       <dbl>
## 1           0.0571 Small          5.1         3.5          1.4         0.2
## 2           0.0667 Small          4.9         3            1.4         0.2
## 3           0.0625 Small          4.7         3.2          1.3         0.2
## # … with 1 more variable: Species <fct>

other functions:

  • dplyr::mutate_all(): Apply a function to every columns.
  • dplyr::mutate_at(): Apply a function to specific column.

4.7 Mutating joins using dplyr::*_join()

iris.meta <- tibble(
    Species = factor(c("setosa","versicolor","virginica")),
    Colony = c("A","A","B") ,
    Ploidy = c("diploid","hexaploid","tetraploid"),
    `Common name` = c("Beachhead iris","Harlequin blueflag","Virginia iris")
)

4.8 Arrange dataset using dplyr::arrange()

iris.tbl %>% arrange(Petal.Length) %>% slice(1:5)
## # A tibble: 5 x 5
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
## 1          4.6         3.6          1           0.2 setosa 
## 2          4.3         3            1.1         0.1 setosa 
## 3          5.8         4            1.2         0.2 setosa 
## 4          5           3.2          1.2         0.2 setosa 
## 5          4.7         3.2          1.3         0.2 setosa
iris.tbl %>% filter(min_rank(Petal.Length) <= 5)
iris.tbl %>% top_n(-5,Petal.Length)

Use desc() to order by high to low.

4.9 Manipulate observations using dplyr::filter()

iris.tbl %>% filter(Sepal.Length < 7) %>% slice(1:5)
## # A tibble: 5 x 5
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
## 1          5.1         3.5          1.4         0.2 setosa 
## 2          4.9         3            1.4         0.2 setosa 
## 3          4.7         3.2          1.3         0.2 setosa 
## 4          4.6         3.1          1.5         0.2 setosa 
## 5          5           3.6          1.4         0.2 setosa
iris.tbl %>% filter(Species ==  "setosa") %>% slice(1:5)
## # A tibble: 5 x 5
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
## 1          5.1         3.5          1.4         0.2 setosa 
## 2          4.9         3            1.4         0.2 setosa 
## 3          4.7         3.2          1.3         0.2 setosa 
## 4          4.6         3.1          1.5         0.2 setosa 
## 5          5           3.6          1.4         0.2 setosa

4.10 Manipulate observations using dplyr::filter()

iris.tbl %>%
    filter(Sepal.Length < 7) %>%
    filter(Species ==  "setosa") %>% slice(1:5)
## # A tibble: 5 x 5
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
## 1          5.1         3.5          1.4         0.2 setosa 
## 2          4.9         3            1.4         0.2 setosa 
## 3          4.7         3.2          1.3         0.2 setosa 
## 4          4.6         3.1          1.5         0.2 setosa 
## 5          5           3.6          1.4         0.2 setosa
iris.tbl %>%
    filter(Sepal.Length < 7 & Species ==  "setosa") %>% slice(1:5)

iris.tbl %>%
    filter(Sepal.Length < 7,Species ==  "setosa") %>% slice(1:5)

4.11 Summarise observations using dplyr::summarise()

iris.tbl %>% summarise(mean = mean(Petal.Length), sd = sd(Petal.Length))
## # A tibble: 1 x 2
##    mean    sd
##   <dbl> <dbl>
## 1  3.76  1.77
iris.tbl %>% select(-Species) %>%summarise_all(mean)
## # A tibble: 1 x 4
##   Sepal.Length Sepal.Width Petal.Length Petal.Width
##          <dbl>       <dbl>        <dbl>       <dbl>
## 1         5.84        3.06         3.76        1.20
iris.tbl %>% summarise(n = n())
## # A tibble: 1 x 1
##       n
##   <int>
## 1   150

4.12 Summarise observations using dplyr::group_by()

iris.tbl %>% group_by(Species) %>% summarise(mean = mean(Petal.Length), sd = sd(Petal.Length))
## # A tibble: 3 x 3
##   Species     mean    sd
##   <fct>      <dbl> <dbl>
## 1 setosa      1.46 0.174
## 2 versicolor  4.26 0.470
## 3 virginica   5.55 0.552
iris.tbl %>% group_by(Species) %>% summarise(n = n())
## # A tibble: 3 x 2
##   Species        n
##   <fct>      <int>
## 1 setosa        50
## 2 versicolor    50
## 3 virginica     50
iris.tbl %>% group_by(Species) %>% tally()
iris.tbl %>% count(Species)

4.13 Summarise observations using dplyr::group_by()

iris.tbl %>% group_by(Species) %>% filter(Petal.Length >= max(Petal.Length))
## # A tibble: 4 x 5
## # Groups:   Species [3]
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species   
##          <dbl>       <dbl>        <dbl>       <dbl> <fct>     
## 1          4.8         3.4          1.9         0.2 setosa    
## 2          5.1         3.8          1.9         0.4 setosa    
## 3          6           2.7          5.1         1.6 versicolor
## 4          7.7         2.6          6.9         2.3 virginica
iris.tbl %>% group_by(Species) %>% top_n(1,Petal.Length)
## # A tibble: 4 x 5
## # Groups:   Species [3]
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species   
##          <dbl>       <dbl>        <dbl>       <dbl> <fct>     
## 1          4.8         3.4          1.9         0.2 setosa    
## 2          5.1         3.8          1.9         0.4 setosa    
## 3          6           2.7          5.1         1.6 versicolor
## 4          7.7         2.6          6.9         2.3 virginica

4.14 Inner join

Inner join: return all rows from x where there are matching values in y, and all columns from x and y. If there are multiple matches between x and y, all combination of the matches are returned.

iris.meta.nosetosa <- iris.meta %>% filter(Species !="setosa")
iris.tbl %>% inner_join(iris.meta.nosetosa,by = "Species") %>% print(n=5)
## # A tibble: 100 x 8
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species Colony Ploidy
##          <dbl>       <dbl>        <dbl>       <dbl> <fct>   <chr>  <chr> 
## 1          7           3.2          4.7         1.4 versic… A      hexap…
## 2          6.4         3.2          4.5         1.5 versic… A      hexap…
## 3          6.9         3.1          4.9         1.5 versic… A      hexap…
## 4          5.5         2.3          4           1.3 versic… A      hexap…
## 5          6.5         2.8          4.6         1.5 versic… A      hexap…
## # … with 95 more rows, and 1 more variable: `Common name` <chr>

4.15 Outer joins

  • A left join keeps all observations in x.
  • A right join keeps all observations in y.
  • A full join keeps all observations in x and y.

4.16 Outer joins

left join:

iris.meta.nosetosa <- iris.meta %>% filter(Species !="setosa")
iris.tbl %>% left_join(iris.meta.nosetosa,by = "Species") %>% print(n=5)
## # A tibble: 150 x 8
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species Colony Ploidy
##          <dbl>       <dbl>        <dbl>       <dbl> <fct>   <chr>  <chr> 
## 1          5.1         3.5          1.4         0.2 setosa  <NA>   <NA>  
## 2          4.9         3            1.4         0.2 setosa  <NA>   <NA>  
## 3          4.7         3.2          1.3         0.2 setosa  <NA>   <NA>  
## 4          4.6         3.1          1.5         0.2 setosa  <NA>   <NA>  
## 5          5           3.6          1.4         0.2 setosa  <NA>   <NA>  
## # … with 145 more rows, and 1 more variable: `Common name` <chr>

right join:

iris.tbl.nosetosa <- iris.tbl %>% filter(Species !="setosa")
iris.tbl.nosetosa %>% right_join(iris.meta,by = "Species") %>% print(n=5)
## # A tibble: 101 x 8
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species Colony Ploidy
##          <dbl>       <dbl>        <dbl>       <dbl> <fct>   <chr>  <chr> 
## 1         NA          NA           NA          NA   setosa  A      diplo…
## 2          7           3.2          4.7         1.4 versic… A      hexap…
## 3          6.4         3.2          4.5         1.5 versic… A      hexap…
## 4          6.9         3.1          4.9         1.5 versic… A      hexap…
## 5          5.5         2.3          4           1.3 versic… A      hexap…
## # … with 96 more rows, and 1 more variable: `Common name` <chr>

4.17 Outer joins

full join:

iris.meta.nosetosa <- iris.meta %>% filter(Species !="setosa")
iris.tbl %>% filter(Species != "virginica") %>% full_join(iris.meta.nosetosa,by = "Species") %>% print(n=3)
## # A tibble: 11 x 8
## # Groups:   Species [3]
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species Colony Ploidy
##           <dbl>       <dbl>        <dbl>       <dbl> <fct>   <chr>  <chr> 
##  1          5.1         3.5          1.4         0.2 setosa  <NA>   <NA>  
##  2          4.9         3            1.4         0.2 setosa  <NA>   <NA>  
##  3          4.7         3.2          1.3         0.2 setosa  <NA>   <NA>  
##  4          4.6         3.1          1.5         0.2 setosa  <NA>   <NA>  
##  5          5           3.6          1.4         0.2 setosa  <NA>   <NA>  
##  6          7           3.2          4.7         1.4 versic… A      hexap…
##  7          6.4         3.2          4.5         1.5 versic… A      hexap…
##  8          6.9         3.1          4.9         1.5 versic… A      hexap…
##  9          5.5         2.3          4           1.3 versic… A      hexap…
## 10          6.5         2.8          4.6         1.5 versic… A      hexap…
## 11         NA          NA           NA          NA   virgin… B      tetra…
## # … with 1 more variable: `Common name` <chr>

4.18 Filtering joins

  • semi_join(x, y) keeps all observations in x that have a match in y.

  • anti_join(x, y) drops all observations in x that have a match in y.

4.19 Filtering joins

treatments.3 %>%
    separate(person,into = c("First_Name","Last_Name"),sep=" ") %>%
    semi_join(treatment.meta,by = c("First_Name","Last_Name"))
##   First_Name Last_Name   treatment result
## 1       John     Smith treatment_a     16
## 2       Jane       Doe treatment_a     22
## 3       John     Smith treatment_b     22
## 4       Jane       Doe treatment_b     19
## 5       John     Smith treatment_c     12
## 6       Jane       Doe treatment_c     22

4.20 Use multiple command together

iris.tbl
## # A tibble: 150 x 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
##  1          5.1         3.5          1.4         0.2 setosa 
##  2          4.9         3            1.4         0.2 setosa 
##  3          4.7         3.2          1.3         0.2 setosa 
##  4          4.6         3.1          1.5         0.2 setosa 
##  5          5           3.6          1.4         0.2 setosa 
##  6          5.4         3.9          1.7         0.4 setosa 
##  7          4.6         3.4          1.4         0.3 setosa 
##  8          5           3.4          1.5         0.2 setosa 
##  9          4.4         2.9          1.4         0.2 setosa 
## 10          4.9         3.1          1.5         0.1 setosa 
## # … with 140 more rows

4.21 Use multiple command together

iris.tbl %>%
    gather(key = "Type",value = "obs",-Species) %>%
    separate(Type,into = c("Type_1","Type_2"),se="\\.")
## # A tibble: 600 x 4
##    Species Type_1 Type_2   obs
##    <fct>   <chr>  <chr>  <dbl>
##  1 setosa  Sepal  Length   5.1
##  2 setosa  Sepal  Length   4.9
##  3 setosa  Sepal  Length   4.7
##  4 setosa  Sepal  Length   4.6
##  5 setosa  Sepal  Length   5  
##  6 setosa  Sepal  Length   5.4
##  7 setosa  Sepal  Length   4.6
##  8 setosa  Sepal  Length   5  
##  9 setosa  Sepal  Length   4.4
## 10 setosa  Sepal  Length   4.9
## # … with 590 more rows

4.22 Use multiple command together

iris.tbl %>%
    gather(key = "Type",value = "obs",-Species) %>%
    separate(Type,into = c("Type_1","Type_2"),se="\\.") %>%
    group_by(Species,Type_1,Type_2) %>%
    mutate(med = median(obs))
## # A tibble: 600 x 5
## # Groups:   Species, Type_1, Type_2 [12]
##    Species Type_1 Type_2   obs   med
##    <fct>   <chr>  <chr>  <dbl> <dbl>
##  1 setosa  Sepal  Length   5.1     5
##  2 setosa  Sepal  Length   4.9     5
##  3 setosa  Sepal  Length   4.7     5
##  4 setosa  Sepal  Length   4.6     5
##  5 setosa  Sepal  Length   5       5
##  6 setosa  Sepal  Length   5.4     5
##  7 setosa  Sepal  Length   4.6     5
##  8 setosa  Sepal  Length   5       5
##  9 setosa  Sepal  Length   4.4     5
## 10 setosa  Sepal  Length   4.9     5
## # … with 590 more rows

4.23 Use multiple command together

iris.tbl %>%
    gather(key = "Type",value = "obs",-Species) %>%
    separate(Type,into = c("Type_1","Type_2"),se="\\.") %>%
    group_by(Species,Type_1,Type_2) %>%
    mutate(med = median(obs)) %>%
    mutate(Size = ifelse(obs < med,"Small","Big")) %>%
    left_join(iris.meta,by = "Species")
## # A tibble: 600 x 9
## # Groups:   Species, Type_1, Type_2 [?]
##    Species Type_1 Type_2   obs   med Size  Colony Ploidy  `Common name` 
##    <fct>   <chr>  <chr>  <dbl> <dbl> <chr> <chr>  <chr>   <chr>         
##  1 setosa  Sepal  Length   5.1     5 Big   A      diploid Beachhead iris
##  2 setosa  Sepal  Length   4.9     5 Small A      diploid Beachhead iris
##  3 setosa  Sepal  Length   4.7     5 Small A      diploid Beachhead iris
##  4 setosa  Sepal  Length   4.6     5 Small A      diploid Beachhead iris
##  5 setosa  Sepal  Length   5       5 Big   A      diploid Beachhead iris
##  6 setosa  Sepal  Length   5.4     5 Big   A      diploid Beachhead iris
##  7 setosa  Sepal  Length   4.6     5 Small A      diploid Beachhead iris
##  8 setosa  Sepal  Length   5       5 Big   A      diploid Beachhead iris
##  9 setosa  Sepal  Length   4.4     5 Small A      diploid Beachhead iris
## 10 setosa  Sepal  Length   4.9     5 Small A      diploid Beachhead iris
## # … with 590 more rows

4.24 Use multiple command together

iris.tbl %>%
    gather(key = "Type",value = "obs",-Species) %>%
    separate(Type,into = c("Type_1","Type_2"),se="\\.") %>%
    group_by(Species,Type_1,Type_2) %>%
    mutate(med = median(obs)) %>%
    mutate(Size = ifelse(obs < med,"Small","Big")) %>%
    left_join(iris.meta,by = "Species") %>%
    arrange(desc(obs)) %>% slice(1)
## # A tibble: 12 x 9
## # Groups:   Species, Type_1, Type_2 [12]
##    Species   Type_1 Type_2   obs   med Size  Colony Ploidy  `Common name`  
##    <fct>     <chr>  <chr>  <dbl> <dbl> <chr> <chr>  <chr>   <chr>          
##  1 setosa    Petal  Length   1.9  1.5  Big   A      diploid Beachhead iris 
##  2 setosa    Petal  Width    0.6  0.2  Big   A      diploid Beachhead iris 
##  3 setosa    Sepal  Length   5.8  5    Big   A      diploid Beachhead iris 
##  4 setosa    Sepal  Width    4.4  3.4  Big   A      diploid Beachhead iris 
##  5 versicol… Petal  Length   5.1  4.35 Big   A      hexapl… Harlequin blue…
##  6 versicol… Petal  Width    1.8  1.3  Big   A      hexapl… Harlequin blue…
##  7 versicol… Sepal  Length   7    5.9  Big   A      hexapl… Harlequin blue…
##  8 versicol… Sepal  Width    3.4  2.8  Big   A      hexapl… Harlequin blue…
##  9 virginica Petal  Length   6.9  5.55 Big   B      tetrap… Virginia iris  
## 10 virginica Petal  Width    2.5  2    Big   B      tetrap… Virginia iris  
## 11 virginica Sepal  Length   7.9  6.5  Big   B      tetrap… Virginia iris  
## 12 virginica Sepal  Width    3.8  3    Big   B      tetrap… Virginia iris

5 Data visualisation using ggplot2

5.1 A base plot

plot(Petal.Width~Petal.Length,data=iris.tbl)

5.2 A ggplot2 plot

iris.tbl %>% ggplot(aes(x=Petal.Width,y=Petal.Length)) +
    geom_point()

5.3 A ggplot2 plot

iris.tbl %>% ggplot(aes(x=Petal.Width,y=Petal.Length)) +
    geom_point() +
    geom_smooth(method = "lm")

5.4 The three key components of every ggplot2

The three components of every graphics :

  • data: the dataset who’s need to be plotted.
  • aesthetics: aesthetics mapping between variables in the data (x,y,visuals properties, …).
  • geoms: one or more layers to render each observations.

5.5 The three key components of every ggplot2

The three components of every graphics :

  • data: the dataset who’s need to be plotted.
  • aesthetics: aesthetics mapping between variables in the data (x,y,visuals properties, …).
  • geoms: one or more layers to render each observations.
p <- iris.tbl %>%# Data
    ggplot(aes(x=Petal.Width,y=Petal.Length)) +# aesthetics
    geom_point()# Layer: points

Add layer : smooth with linear model

p + geom_smooth(method = "lm")

More you add components with +, more you’ll build sophisticated plots.

5.6 Add more aesthetics

p <- iris.tbl %>%# Data
    ggplot(aes(x=Petal.Width,y=Petal.Length,col=Species)) +# aesthetics
    geom_point()# Layer: points
print(p)

5.7 Add more aesthetics

p + geom_smooth(method = "lm")

5.8 Add more aesthetics

p <- iris.tbl %>%# Data
    ggplot(aes(x=Petal.Width,y=Petal.Length)) +# aesthetics
    geom_point(aes(col=Species)) +# Layer: points
    geom_smooth(method = "lm")
print(p)

5.9 Other aesthetics attributes

p <- iris.tbl %>%# Data
    ggplot(aes(x=Petal.Width,y=Petal.Length)) +# aesthetics
    geom_point(aes(shape=Species))# Layer: points
print(p)

5.10 Other aesthetics attributes

p <- iris.tbl %>%# Data
    ggplot(aes(x=Petal.Width,y=Petal.Length)) +# aesthetics
    geom_point(aes(col=Species,size = Sepal.Length))# Layer: points
print(p)

5.11 Use fixed value

p <- iris.tbl %>%# Data
    ggplot(aes(x=Petal.Width,y=Petal.Length)) +# aesthetics
    geom_point(col="blue",size=4) +# Layer: points
    geom_smooth(method = "lm",col="red",fill="orange")
print(p)

5.12 Plot geoms

5.13 Facetting

Facetting:

  • facet_grid(variable~foliaces) : Display all possibility even if some plots are empty.
  • facet_wrap(variable~foliaces) : Display only the plots having actual values.

5.14 Graphical options

p <- iris.tbl %>%# Data
    ggplot(aes(x=Petal.Width,y=Petal.Length)) +# aesthetics
    geom_point(aes(col=Species,size = Sepal.Length)) +# Layer: points
    scale_color_brewer(palette="Set3") #A new palette
print(p)

5.15 Graphical options

p <- iris.tbl %>%# Data
    ggplot(aes(x=Petal.Width,y=Petal.Length)) +# aesthetics
    geom_point(aes(col=Species,size = Sepal.Length)) +# Layer: points
    scale_color_manual(values = c("red","blue","green"),  #A new palette
                       labels = c("Setosa","Versicolor","Virginica"))
print(p)

5.16 Themes

5.17 Themes

5.18 Themes

5.19 Extensions

  • ggplot2 extensions: This site tracks and lists ggplot2 extensions developed by R users in the community. https://www.ggplot2-exts.org/.
  • cowplot: Arranging graphs into a grid and improve plot design.
  • ggpubr & __ ggstatsplot__: Publication ready plots & add statistical tests.
  • ggrepel: Automatic label placement.
  • esquisse: Explore and Visualize Your Data Interactively with ggplot2.
  • ggvis: Create rich interactive graphics with a syntax similar in spirit to ggplot2.

5.20 Why I use ggplot2

  • The “default” output is much nicer than with base graphics.
  • Automatic legend, color with mapping to the variables.
  • Easy to combine multiple plot.
  • Easy facetting.
  • A lot of themes.
  • A lot of extensions …